Dong-Cheon Lee

Comprehensive Analysis of Sensor Modeling Alternatives for High Resolution Imaging Satellites

High-resolution imaging satellites are a valuable and cost effective data acquisition tool for a variety of mapping and GIS applications such as topographic mapping, map updating, orthophoto generation, environmental monitoring, and change detection. Sensor modeling that describes the mathematical relationship between corresponding scene and object coordinates is a prerequisite procedure prior to manipulating the acquired imagery from such systems for mapping purposes. Rigorous and approximate sensor models are the two alternatives for describing the mathematics of the involved imaging process. The former explicitly involves the internal and external characteristics of the imaging sensor to faithfully represent the geometry of the scene formation. On the other hand, approximate modeling can be divided into two categories. The first category simplifies the rigorous model after making some assumptions about the system’s trajectory and/or object space. Gupta and Hartley’s model, parallel projection, self-calibrating direct linear transformation, and modified parallel projection are examples of this category. Other approximate models are based on empirical formulation of the scene-to-ground mathematical relationship. This category includes among others, the well-known Rational Function Model (RFM). This paper addresses several aspects of sensor modeling. Namely, it deals with the expected accuracy from rigorous modeling of imaging satellites as it relates to the number of available ground control points, comparative analysis of approximate and rigorous sensor models, robustness of the reconstruction process against biases in the available sensor characteristics, and impact of incorporating multi-source imagery in a single triangulation mechanism. Following a brief theoretical background, these issues will be presented through experimental results from real datasets captured by satellite and aerial imaging platforms.

Alternative Methodologies for the Internal Quality Control of Parallel LiDAR Strips

Light Detection and Ranging (LiDAR) systems have been widely adopted for the acquisition of dense and accurate topographic data over extended areas. Although the utilization of this technology has increased in different applications, the development of standard methodologies for the quality control (QC) of LiDAR data has not followed the same trend. In other words, a lack in reliable, practical, cost-effective, and commonly acceptable QC procedures is evident. A frequently adopted procedure for QC is comparing the LiDAR data to ground control points. Aside from being expensive, this approach is not accurate enough for the verification of horizontal accuracy, unless specifically designed LiDAR targets are used. This paper is dedicated to providing accurate, economical, and convenient internal QC procedures for the evaluation of LiDAR data, which is captured from parallel flight lines. The underlying concept of the proposed methodologies is that, in the absence of systematic and random errors in system parameters and measurements, conjugate surface elements in overlapping strips should perfectly match each other. Consistent incompatibilities and the quality of fit between conjugate surface elements in overlapping strips can be used to detect systematic errors in the system parameters/measurements and to evaluate the noise level in the LiDAR point cloud, respectively. Experimental results from real data demonstrate that all the proposed methods, with one exception, produce compatible estimates of systematic discrepancies between the involved data sets, as well as good quantification of inherent noise.

Error Budget of Lidar Systems and Quality Control of the Derived Data

Lidar systems have been widely adopted for the acquisition of dense and accurate topographic data over extended areas. Although the utilization of this technology has increased in different applications, the development of standard methodologies for the quality assurance of lidar systems and quality control of the derived data has not followed the same trend. In other words, a lack of reliable, practical, cost-effective, and commonly-acceptable methods for quality evaluation is evident. A frequently adopted procedure for quality evaluation is the comparison between lidar data and ground control points. Besides being expensive, this approach is not accurate enough for the verification of the horizontal accuracy, which is known to be worse than the vertical accuracy. This paper is dedicated to providing an accurate, economical, and convenient quality control methodology for the evaluation of lidar data. The paper starts with a brief discussion of the lidar mathematical model, which is followed by an analysis of possible random and systematic errors and their impact on the resulting surface. Based on the discussion of error sources and their impact, a tool for evaluating the quality of the derived surface is proposed. In addition to the verification of the data quality, the proposed method can be used for evaluating the system parameters and measurements. Experimental results from simulated and real data demonstrate the feasibility of the proposed tool.

Geodetic datum transformation to the global geocentric datum for seas and islands around Korea

According to revisions of survey law taking effect on January 1, 2003, the Korean geodetic datum has been changed from a local geodetic to a world geodetic system. Since the datum change demands a geographical data transformation, the National Geographic Information Institute has established step-by-step plans for the transformation of the land data constructed through the National GIS Project, and it is in progress. For maritime data, however, no detailed transformation plan has been established yet. Therefore, it is necessary to analyze the maritime geographic data obtained through the Maritime GIS project and set up the data transformation scheme to a world geodetic system. In this study, the datum transformation parameters especially for the maritime geographical data are determined. From database constructed through MGIS, a total of 492 coordinate pairs were used in parameter determination initially. At this stage, three popular seven parameter transformation models, Bursa-Wolf, Molodensky and Veis model, and the multi regression equation are applied, and the transformation parameters from the Molodensky model are selected for its accuracy and consistency with the land data transformation method. To eliminate the local bias caused by the nonequally distributed stations, a network optimization is applied and 42 stations are selected to determine the final transformation parameters. The distortion after applying the similarity transformation is modeled through a least squares collocation with Gaussian model, and high accuracy better than 15 cm in coordinate transformation is obtained.

Line-of-sight vector adjustment model for geopositioning of SPOT-5 stereo images

We formulate and present a new geopositioning method for SPOT-5 High-Resolution Geometric (HRG) stereo images, named the line-of-sight (LOS) vector adjustment model. It is applicable to satellites that move along a well-defined close-to-circular elliptical orbit with a predicted orbit close to true. SPOT-5 satisfies these requirements because it has the improved capability of providing accurate satellite attitude and a look angle for each detector. The method’s core idea is that only the LOS vector was adjusted when correcting the geometric distortion of SPOT-5 imagery. One advantage of this method is that it achieves high geopositioning accuracy with a limited number of ground control points (GCPs). Although a minimum of three GCPs is needed for processing, a test result satisfied the accuracy requirement within one pixel of a SPOT-5 panchromatic image even with only three GCPs. The performance in terms of root mean square error (RMSE) improved as the number of GCPs increased. Five GCPs were found to be the optimal number in the practical application of the LOS vector adjustment model. Using five GCPs, the RMSEs were 0.48 m and 0.64 m in planimetry and height, respectively. The test results indicate that the proposed method is superior to the bundle adjustment method for the geopositioning of SPOT-5 HRG stereo images.

Absolute kinematic GPS positioning for remote area

Method and the results of investigations to determine the position vector of a static or moving vehicle using the Global Positioning System (GPS) in the absolute (point) positioning mode is presented. The motivation of this research is to determine the position using GPS in remote area such as polar region and open sea in which it is difficult/impossible to set up a reference station. Starting from the basic positioning scheme with code observation, the most accurate positioning strategy using the phase is described. The achievable accuracy under no SA for each GPS data processing method is also assigned. The main strategy for the highest accuracy absolute positioning is to estimate GPS satellite clock errors independently, thus obviating the between-station differencing. The GPS clock errors are estimated at 30-second intervals using International GPS Service (IGS) orbits and stations. The clock error estimates are then used in an absolute positioning algorithm to determine the coordinates without any other reference site, which is the case for remote area surveys. Static and kinematic GPS data at 1-second sampling rate were processed and compared with the known values and the corresponding DGPS solutions. For the static case, an IGS station was assumed as unknown and its coordinates were estimated. For the kinematic case, the data from an aircraft survey near polar area were tested.

True orthoimage generation by mutual recovery of occlusion areas

Demand for orthoimages is increasing as a crucial component of geographic information systems (GISs). Orthoimages are geometrically equivalent to planimetric maps, which show true geographic locations of terrain features. To produce orthoimages, geometric distortions from camera tilt and relief displacement from perspective images must be corrected. Traditionally, removing such distortions has been accomplished by differential rectification in a pixel-by-pixel fashion. However, this method cannot produce true orthoimages because of the double-mapping problem. We propose a method of generating patch-based true orthoimages for surface patches in buildings. The proposed method utilizes three-dimensional (3D) building model data. Patches from the data were projected onto aerial images to extract image patches and analysis of the superstructures was performed. Because orthoimages are generated for each building, the orthoimage quality is enhanced when using building data with a high level of detail. Instead of performing the complex visibility analysis of existing approaches, this article identifies occlusion areas based on unit surfaces of buildings and presents mutual recovery of occlusions using multiple images. To evaluate the feasibility of the method, experiments were performed with real datasets: (1) a building with a dome superstructure, (2) high-rise buildings close to each other, and (3) buildings with various shapes.

Automatic recovery of building heights from aerial digital images

Recently in the field of telecommunication, there is much interest in geo-surface characteristics of urban areas. Geophysical properties of urban features are now incorporated with accurate positional information to model the telecommunication environment. In this study, three dimensional buildings are geometrically reconstructed from existing vector maps and aerial images. Accurate digital vector maps are easily available in Korea. However existing maps, which had been produced for GIS applications, do not have height information which is critical to three dimensional building reconstruction. Image matching techniques were applied to aerial image stereopairs to automatically extract the height information of buildings. Planimetric coordinates from vector maps were used as initial guides in the process. Future studies can be undertaken to link geophysical properties to the three dimensional spatial objects reconstructed from this study thus bringing the telecommunication environment model closer to reality

Relationship between high white blood cell count and insulin resistance (HOMA-IR) in Korean children and adolescents: Korean National Health and Nutrition Examination Survey 2008–2010

BACKGROUND AND AIMS Increasing evidence has indicated that insulin resistance is associated with inflammation. However, few studies have investigated the association between white blood cell (WBC) count and insulin resistance, as measured by a homeostasis model assessment of insulin resistance (HOMA-IR) in a general pediatric population. This study aimed to examine the association between WBC count and insulin resistance as measured by HOMA-IR in a nationally representative sample of children and adolescents. METHODS AND RESULTS In total, 2761 participants (1479 boys and 1282 girls) aged 10-18 years were selected from the 2008-2010 Korean National Health and Nutrition Examination Survey. Insulin resistance was defined as a HOMA-IR value greater than the 90th percentile. The odds ratios and 95% confidence intervals for insulin resistance were determined using multiple logistic regression analysis. The mean values of most cardiometabolic variables tended to increase proportionally with WBC count quartiles. The prevalence of insulin resistance significantly increased in accordance with WBC count quartiles in both boys and girls. Compared to individuals in the lowest WBC count quartile, the odds ratio for insulin resistance for individuals in the highest quartile was 2.84 in boys and 3.20 in girls, after adjusting for age, systolic blood pressure, body mass index, and waist circumference. CONCLUSION A higher WBC count was positively associated with an increased risk of insulin resistance in Korean children and adolescents. This study suggests that WBC count could facilitate the identification of children and adolescents with insulin resistance.

Electromagnetic Land Surface Classification Through Integration of Optical and Radar Remote Sensing Data

We present a nonhierarchical electromagnetic (EM) land surface classification method through the integration of satellite multispectral high-resolution optical and polarized radar images of central Alberta near the Saskatchewan border. We implement a conventional supervised land surface classification method and a principal component analysis to a QuickBird image. The EM properties are then assigned to the classified surfaces to produce hierarchical EM land classification maps. To further classify a hierarchical EM surface (i.e., dielectric constant), we calculate the root-mean-square surface height with a Shuttle Radar Topography Mission 3-arc-second digital elevation model and the temperatures from a thermal band of a Landsat-5 Thematic Mapper image. We also calculate the backscattering coefficients from the Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar image. Using these estimated values, we calculate the intrinsic weighting factors with the Dubois (1995) model for less vegetated land areas and the Ulaby (1986) model for open water areas. By applying these weighting factors to the hierarchical EM surface, we generate a nonhierarchical higher resolution EM surface map of the study area.